Table A1 Focus overview of analyzed...

Table A1

Focus overview of analyzed articles

Core author(s)	Article	FWCI	Target region	Aim of the article	Survey period	Sample size
Kliestik, Kovacova (Misankova), Valaskova	Kliestik et al. (2017)	28.44	Slovakia	To design and assess a novel tool for bankruptcy prediction	2012–2015	265,347
Kliestik, Kovacova	Kovacova and Kliestik (2017)	7.25	Slovakia	To construct models for the bankruptcy prediction of Slovak companies and compare the overall predictive ability of the two developed models	2015	1,000
Kliestik, Kovacova	Kovacova et al. (2018)	2.25	Slovakia	To test the validity of prediction models developed as partial results of our research project	2015–2016	27,029
Kliestik, Kovacova, Valaskova	Valaskova et al. (2018)	26.19	Slovakia	To assess the financial risks of Slovak entities, realized by identifying significant factors and determinants affecting the prosperity of Slovak companies	2015	62,533
Kliestik, Kovacova, Valaskova, Vrbka	Kliestik et al. (2020)	15.44	Slovakia, the Czech Republic, Poland, Hungary, Romania, Lithuania, Latvia, Estonia, Croatia, Russia, Ukraine and Belarus	To analyse and compare financial ratios used in the models of transition countries	1993–2018	180 models (not companies) were analyzed
Kliestik, Vrbka	Kliestik et al. (2018)	4.47	V4 (Czech Rep., Hungary, Poland, Slovakia)	To develop a model to reveal the unhealthy development of the enterprises in V4 countries, which is done by the multiple discriminant analysis	2015–2016	449,781
Kovacova (Misankova)	Gavurova et al. (2017)	3.92	Slovakia	Assessment of four bankruptcy prediction models to determine the most appropriate model	2009–2014	700
Kovacova, Valaskova	Kovacova et al. (2019)	N/A	V4 (Czech Rep., Hungary, Poland, Slovakia)	To provide deep insight and analyse the bankruptcy prediction models developed in countries of Visegrad four, emphasizing methods applied and explanatory variables used in these models, and evaluate them through appropriate statistical methods	Not stated	103 prediction models developed in V4 countries
Kovacova, Vrbka	Podhorska et al. (2020)	N/A	Emerging markets including 17 countries from Europe	To create a comprehensive prediction model of enterprise financial distress based on decision trees under emerging market conditions. The model also contains three dummy variables (country, size of enterprise and NACE classification) and countries’ GDP data	2015–2016	2,359,731
Valaskova	Valaskova et al. (2020)	N/A	Slovakia	To portray the bankruptcy models (eight) developed in conditions of the Slovak republic, especially in the agriculture sector, verify their predictive ability using divergent statistical methods, and explore the importance of financial ratios in the prediction of financial stability	2016–2018	3,329
Altman, Laitinen	Altman et al. (2017)	18.03	31 European + 3 non-European countries (USA, China, Colombia)	To deliberate on categorizing the Z-Score model in terms of forecasting bankruptcy	2002–2010	2,640,778
Altman, Laitinen	Altman, Iwanicz-Drozdowska, Laitinen, and Suvas (2016)	1.25	Finland	To evaluate the effectiveness of financial and nonfinancial variables in the long-term perspective	2004–2013	59,099
Altman	Barboza et al. (2017)	14.88	North America	To test machine learning models to predict bankruptcy one year before the event, and compare their performance with other models	1985–2013	41,741
Altman	Altman (2018)	2.41	Not mentioned	To assess the fundamental and stats elements of Altman’s Z-score model presented in 1968	1968–2018	No real data
Altman	Altman (2018)	2.03	Not mentioned	To discuss many implementations of the Z-score	1968–2018	No real data
Altman, Laitinen	Altman et al. (2020)	1.93	Finland	To compare the accuracy and efficiency of five different estimation methods for predicting the financial distress of small and medium-sized enterprises	2004–2013	48,916
Laitinen	Laitinen and Lukason (2014)	1.89	Finland, Estonia	Considering “the novel topic of comparing firm failure processes between different countries.”	2002–2009	140
Laitinen	Laitinen and Suvas (2016)	2.07	EU–26	The objective is to investigate the influence of Hofstede’s original cultural dimensions on financial distress prediction	2002–2010	1,278,362
Laitinen	Lukason and Laitinen (2019)	1.36	EU (Italy, France, Spain, Romania, Hungary)	The paper aims to extract firm failure processes (FFPs) by using failure risk and ranking the importance of failure risk contributors for different stages of FFPs	N/A	1,234
Laitinen	Muñoz-Izquierdo et al. (2020)	1.86	Spain	To empirically analyse the usefulness of combining accounting and auditing data to predict corporate financial distress. Concretely, to examine whether audit report information incrementally predicts distress over a traditional accounting model: the Altman’s Z-Score model	2004–2014	808
Jabeur	Jabeur et al. (2021)	8.30	France	To propose a new gradient boosting technique for bankruptcy prediction, namely, CatBoost	2014–2016	133
Jabeur	Ben Jabeur (2017)	2.03	France	To improve the LR in the presence of highly correlated data, by using a PLS-LR that offers a significant alternative by allowing, among other advantages, in considering the action of the existing correlation	2006–2008	800
Jabeur	Jabeur and Fahmi (2018)	1.82	France	To present a model to predict financial distress in French companies	2006–2008	800
Jabeur	Stef and Jabeur (2018)	0.53	France	To determine if nonfinancial variables such as the number of new firms can represent a useful tool for forecasting a firm’s liquidation	2006–2008	825
Jabeur	Ben Jabeur, Stef, and Carmona (2022)	4.44	France	An improved Extreme Gradient Boosting (XGBoost) algorithm based on feature importance selection (FS-XGBoost) is proposed to predict corporate failure	2014–2017	1,850
Tsai	Liang et al. (2016)	6.17	Taiwan	To assess the prediction performance obtained by combining multiple financial ratios and corporate governance indicators	1999–2009	478
Tsai	Tsai and Cheng (2012)	1.41	Australia, Germany, Japan	To examine the performance of bankruptcy prediction models after removing several outlier volumes	N/A	4,778
Tsai	Tsai and Hsu (2013)	1.14	Australia, Germany, Japan	To present a meta-learning framework to predict bankruptcy	N/A	2,343
Tsai	Liang, Tsai, and Wu (2015)	2.78	Australia, Germany, Taiwan, China	A comprehensive study examines the effect of performing filter and wrapper-based feature selection methods on financial distress prediction. In addition, the impact of feature selection on the prediction models obtained using various classification techniques is also investigated	N/A	2,818
Tsai	Liang et al. (2020)	1.65	USA	To construct a bankruptcy prediction model based on multiple financial ratios and corporate governance indicators	1996–2014	286
Tsai	Tsai et al. (2021)	1.41	Not exactly specified	To compare the performance of three feature selection algorithms, three instance selection algorithms, four classification algorithms, and two ensemble learning techniques	N/A	242,429
Jones	Jones et al. (2017)	6.16	USA	Based on a large sample of US corporate bankruptcies, we examine the predictive performance of 16 classifiers, ranging from the most restrictive classifiers (such as logit, probit and linear discriminant analysis) to more advanced techniques such as neural networks, support vector machines (SVMs) and “new age” statistical learning models including generalized boosting, AdaBoost and random forests	2000–N/A	3,111
Jones	Peat and Jones (2012)	N/A	Australia	The study adds to current debates by investigating the performance of N.N.s in the context of forecast combination. Furthermore, to test the performance of the N.N. model with the most widely used discrete choice model in the bankruptcy literature, logistic regression	Period 1: 2000–2002, Period 2: 2003+	558 max/period (different samples for different periods)
Jones	Jones (2017)	3.91	USA	To outline a conspicuous trend in the literature by applying the gradient boosting model	1987–2013	1,115
Jones	Cheng, Jones, and Moser (2018)	0.27	USA	To examine the trading behaviour of U.S. corporate insiders and certain groups of institutional investors (short-term, transient, top-performing, and those with fiduciary responsibility) in the eight quarters leading up to a U.S. firm bankruptcy filing	1992–2012	610
Jones	Jones and Wang (2019)	2.16	Whole world	The study utilizes an advanced machine learning method known as TreeNet(R) (Salford Systems, 2017) to predict various private company failure states, ranging from binary settings (i.e. failed vs non-failed) to more complex multi-class settings with up to five states of failure	2009–2013	4,922,271
Jones	Alam, Gao, and Jones (2021)	0.97	North America	To propose a deep learning model of firm failure prediction and compare it to the traditional prediction model	2001–2018	641,667
Li, Sun	Sun, Li, Huang, and He (2014)	5.97	China	To compile a complete summary, analysis and evaluation of the current literature on financial distress prediction (FDP)	N/A	No real data
Li, Sun	Li and Sun (2009)	2.06	China	To construct of hybrid case-based reasoning model and to test the performance	N/A	153
Li, Sun	Li and Sun (2011a)	N/A	China	To explain the data mining technique of two-step clustering; to introduce a new mining method	N/A	266
Li, Sun	Li and Sun (2011b)	1.30	China	To explain the necessity to base such case-based reasoning ensemble (CBRE) prediction technique on random similarity functions (RSF)	N/A	313
Li, Sun	Li and Sun (2011c)	1.52	China	To construct a principal-component case-based reasoning ensemble (PC-CBR-E) model	N/A	270
Li, Sun	Li and Sun (2011d)	0.36	China	To compare different models using SVM techniques	N/A	153
Li, Sun	Li, Lee, Zhou, and Sun (2011)	1.17	China	To construct a new model based on random subspace binary logistic regression analysis	N/A	270
Li, Sun	Li and Sun (2012)	1.01	China	To compare the CBR ensemble with MDA, logistic regression, and classical CBR algorithm	N/A	153
Li, Sun	Li, Hong, He, Xu, and Sun (2013)	0.40	China	To construct a small sample-oriented case-based kernel predictive method (SSOCBKPM)	N/A	200
Li, Sun	Li, Li, Wu, and Sun (2014)	2.42	China	To verify statistically a performance of statistic-based wrapper based on SVM methods	N/A	668
Li, Sun	Sun et al. (2014)	0.97	China	To explore the “imbalanced FDP based on SVM.”	Sample 1: 2010–2012 Sample 2: 2012–2013	427
Li, Sun	Sun et al. (2016)	0.79	China, world	To propose an approach for dynamic evaluation and prediction of financial distress based on the entropy-based weighting (EBW), the support vector machine (SVM) and an enterprise’s vertical sliding time window (VSTW)	2006–2010	5
Li, Sun	Sun et al. (2019)	0.56	China	To replicate the Campbell, Hilscher, and Szilagyi (2008) bankruptcy prediction model and add additional terms for the absolute value of changes in the percentage ownership by corporate insiders over the previous six months or changes in ownership by specific groups of institutional investors	2000–2015	486
Li	Li, Hong, Zhou, and Yu (2015)	0.12	China	To compare pure SVM, hybrid SVM, SVM ensemble, and hybrid SVM ensemble	N/A	551
Li	Li, Xu, and Yu (2017)	0.57	China	To provide a “feasible approach to handle possible mixed information caused by oversampling; mixed sample modelling (MSM).”	N/A	No real data
du Jardin	du Jardin (2016)	3.51	France	To present a new model of bankruptcy prediction based on ensembles of models	2002–2012 Learning samples 2002–2011 (one set per year), testing samples 2003–2012 (one set per year)	337,400
du Jardin	du Jardin and Séverin (2012)	0.82	France	To introduce a new way of using a Kohonen map as a prediction model	Period 1: 1998–2000, period 2: 2000–2002, period 3: 2002–2004	11,540
du Jardin	du Jardin (2018)	1.62	France	To propose a new bankruptcy prediction model that relies on estimating failure patterns that are quantified with ensembles of Kohonen maps	2007–2014	6,120
du Jardin	du Jardin et al. (2019)	1.35	France	To present a new measure that helps improve bankrupt models’ accuracy by using a method to embody earnings management	Period 1: 2006–2007, Period 2: 2009–2010, Period 3: 2011–2012	14,220 max (different samples for different periods)
du Jardin	du Jardin (2021)	1.25	France	To present a new method of bankruptcy prediction based on modelling firms’ history with a self-organizing map. To propose an approach that relies on a particular modelling of firm history using self-organizing neural networks and segmentation of the data space, which makes it possible to typify subsets of firms that share a common evolution of their financial situation over time	Period 1: 2000–2003, Period 2: 2004–2007, Period 3: 2007–2011, Period 4: 2011–2015	293,840
du Jardin	du Jardin (2021)	2.82	France	To present a new firm-failure forecasting method using an ensemble of self-organizing neural networks	2008–2015	470,330
Korol	Korol (2013)	2.61	Poland, Latin America (Mexico, Argentina, Brazil, Chile, Peru)	To compare “the effectiveness of twelve different early warning models.”	Poland 2000–2007, Latin America 1996–2009	245
Korol	Korol and Kolodi (2011)	1.15	Poland	To present a fuzzy logic-based system	1999–2005	132
Korol	Korol (2018)	0.16	7 EU countries, ten non-EU countries	To evaluate the effectiveness of the 13 fuzzy logic models	Sample 1: 1999–2007, Sample 2: 2000–2009, Sample 3: 1999–2009	166
Korol	Korol (2019)	N/A	EU	To develop and evaluate dynamic bankruptcy prediction models for European enterprises	2004–2017, the period ten years before bankruptcy	600
Karas, Režňáková	Karas and Režňáková (2017)	1.84	Czech Republic	To verify whether bankruptcy predictors are specific in terms of industry or time	2004–2013 (the concerned companies went bankrupt 2008–2013)	34,533
Karas, Režňáková	Režňáková and Karas (2015)	0.97	V4 (Czech Rep., Hungary, Poland, Slovakia)	To test the predictive capability of the original version of the Altman model in an environment different from the environment of its origin and to explore its transferability to a different economic environment	2007–2012	5,977
Karas, Režňáková	Karas et al. (2017)	0.50	Czech Republic	To analyse the current accuracy of four traditional bankruptcy prediction models (the revised Z-score model, Altman-Sabato’s model in both versions – with unlogged and logged predictors, and IN 05 model) in agriculture	2011–2014	475
Karas, Režňáková	Karas and Režňáková (2017)	1.16	Czech Republic	To create a bankruptcy prediction model based on the data from construction companies in the Czech Republic	2011–2014	654
Karas, Režňáková	Karas and Režňáková (2018)	0.53	Czech Republic	To analyze the usefulness of information about the past development of a company’s financial situation in predicting bankruptcy	2011–2014	1,355
Karas	Karas and Srbová (2019)	0.52	Czech Republic	To test the current accuracies of five selected bankruptcy models in predicting the bankruptcy of construction companies and to create a new model designed specifically for this branch	2006–2015 (the concerned companies went bankrupt 2011–2015)	4,420
Karas, Režňáková	Karas and Režňáková (2020)	0.41	Czech Republic	To introduce a new hybrid model incorporating solely cashflow-based indicators (three model versions were derived)	2013–2018	4,350
Karas, Režňáková	Karas and Režňáková (2021)	1.25	EU-28	Construct a default prediction model incorporating factors considered internal or external manifestations of the financial constraint situation. For example, authors use the Cox semiparametric model, leaving the baseline hazard rate unspecified and employing macroeconomic variables as explanatory variables	2014–2019	213,731

Core author(s)	Article	FWCI	Target region	Aim of the article	Survey period	Sample size
Kliestik, Kovacova (Misankova), Valaskova	Kliestik et al. (2017)	28.44	Slovakia	To design and assess a novel tool for bankruptcy prediction	2012–2015	265,347
Kliestik, Kovacova	Kovacova and Kliestik (2017)	7.25	Slovakia	To construct models for the bankruptcy prediction of Slovak companies and compare the overall predictive ability of the two developed models	2015	1,000
Kliestik, Kovacova	Kovacova et al. (2018)	2.25	Slovakia	To test the validity of prediction models developed as partial results of our research project	2015–2016	27,029
Kliestik, Kovacova, Valaskova	Valaskova et al. (2018)	26.19	Slovakia	To assess the financial risks of Slovak entities, realized by identifying significant factors and determinants affecting the prosperity of Slovak companies	2015	62,533
Kliestik, Kovacova, Valaskova, Vrbka	Kliestik et al. (2020)	15.44	Slovakia, the CzechRepublic, Poland, Hungary, Romania, Lithuania, Latvia, Estonia, Croatia, Russia, Ukraine and Belarus	To analyse and compare financial ratios used in the models of transition countries	1993–2018	180 models (not companies) were analyzed
Kliestik, Vrbka	Kliestik et al. (2018)	4.47	V4 (Czech Rep., Hungary, Poland, Slovakia)	To develop a model to reveal the unhealthy development of the enterprises in V4 countries, which is done by the multiple discriminant analysis	2015–2016	449,781
Kovacova (Misankova)	Gavurova et al. (2017)	3.92	Slovakia	Assessment of four bankruptcy prediction models to determine the most appropriate model	2009–2014	700
Kovacova, Valaskova	Kovacova et al. (2019)	N/A	V4 (Czech Rep., Hungary, Poland, Slovakia)	To provide deep insight and analyse the bankruptcy prediction models developed in countries of Visegrad four, emphasizing methods applied and explanatory variables used in these models, and evaluate them through appropriate statistical methods	Not stated	103 prediction models developed in V4 countries
Kovacova, Vrbka	Podhorska et al. (2020)	N/A	Emerging markets including 17 countries from Europe	To create a comprehensive prediction model of enterprise financial distress based on decision trees under emerging market conditions. The model also contains three dummy variables (country, size of enterprise and NACE classification) and countries’ GDP data	2015–2016	2,359,731
Valaskova	Valaskova et al. (2020)	N/A	Slovakia	To portray the bankruptcy models (eight) developed in conditions of the Slovak republic, especially in the agriculture sector, verify their predictive ability using divergent statistical methods, and explore the importance of financial ratios in the prediction of financial stability	2016–2018	3,329
Altman, Laitinen	Altman et al. (2017)	18.03	31 European + 3 non-European countries (USA, China, Colombia)	To deliberate on categorizing the Z-Score model in terms of forecasting bankruptcy	2002–2010	2,640,778
Altman, Laitinen	Altman, Iwanicz-Drozdowska, Laitinen, and Suvas (2016)	1.25	Finland	To evaluate the effectiveness of financial and nonfinancial variables in the long-term perspective	2004–2013	59,099
Altman	Barboza et al. (2017)	14.88	North America	To test machine learning models to predict bankruptcy one year before the event, and compare their performance with other models	1985–2013	41,741
Altman	Altman (2018)	2.41	Not mentioned	To assess the fundamental and stats elements of Altman’s Z-score model presented in 1968	1968–2018	No real data
Altman	Altman (2018)	2.03	Not mentioned	To discuss many implementations of the Z-score	1968–2018	No real data
Altman, Laitinen	Altman et al. (2020)	1.93	Finland	To compare the accuracy and efficiency of five different estimation methods for predicting the financial distress of small and medium-sized enterprises	2004–2013	48,916
Laitinen	Laitinen and Lukason (2014)	1.89	Finland, Estonia	Considering “the novel topic of comparing firm failure processes between different countries.”	2002–2009	140
Laitinen	Laitinen and Suvas (2016)	2.07	EU–26	The objective is to investigate the influence of Hofstede’s original cultural dimensions on financial distress prediction	2002–2010	1,278,362
Laitinen	Lukason and Laitinen (2019)	1.36	EU (Italy, France, Spain, Romania, Hungary)	The paper aims to extract firm failure processes (FFPs) by using failure risk and ranking the importance of failure risk contributors for different stages of FFPs	N/A	1,234
Laitinen	Muñoz-Izquierdo et al. (2020)	1.86	Spain	To empirically analyse the usefulness of combining accounting and auditing data to predict corporate financial distress. Concretely, to examine whether audit report information incrementally predicts distress over a traditional accounting model: the Altman’s Z-Score model	2004–2014	808
Jabeur	Jabeur et al. (2021)	8.30	France	To propose a new gradient boosting technique for bankruptcy prediction, namely, CatBoost	2014–2016	133
Jabeur	Ben Jabeur (2017)	2.03	France	To improve the LR in the presence of highly correlated data, by using a PLS-LR that offers a significant alternative by allowing, among other advantages, in considering the action of the existing correlation	2006–2008	800
Jabeur	Jabeur and Fahmi (2018)	1.82	France	To present a model to predict financial distress in French companies	2006–2008	800
Jabeur	Stef and Jabeur (2018)	0.53	France	To determine if nonfinancial variables such as the number of new firms can represent a useful tool for forecasting a firm’s liquidation	2006–2008	825
Jabeur	Ben Jabeur, Stef, and Carmona (2022)	4.44	France	An improved Extreme Gradient Boosting (XGBoost) algorithm based on feature importance selection (FS-XGBoost) is proposed to predict corporate failure	2014–2017	1,850
Tsai	Liang et al. (2016)	6.17	Taiwan	To assess the prediction performance obtained by combining multiple financial ratios and corporate governance indicators	1999–2009	478
Tsai	Tsai and Cheng (2012)	1.41	Australia, Germany, Japan	To examine the performance of bankruptcy prediction models after removing several outlier volumes	N/A	4,778
Tsai	Tsai and Hsu (2013)	1.14	Australia, Germany, Japan	To present a meta-learning framework to predict bankruptcy	N/A	2,343
Tsai	Liang, Tsai, and Wu (2015)	2.78	Australia, Germany, Taiwan, China	A comprehensive study examines the effect of performing filter and wrapper-based feature selection methods on financial distress prediction. In addition, the impact of feature selection on the prediction models obtained using various classification techniques is also investigated	N/A	2,818
Tsai	Liang et al. (2020)	1.65	USA	To construct a bankruptcy prediction model based on multiple financial ratios and corporate governance indicators	1996–2014	286
Tsai	Tsai et al. (2021)	1.41	Not exactly specified	To compare the performance of three feature selection algorithms, three instance selection algorithms, four classification algorithms, and two ensemble learning techniques	N/A	242,429
Jones	Jones et al. (2017)	6.16	USA	Based on a large sample of US corporate bankruptcies, we examine the predictive performance of 16 classifiers, ranging from the most restrictive classifiers (such as logit, probit and linear discriminant analysis) to more advanced techniques such as neural networks, support vector machines (SVMs) and “new age” statistical learning models including generalized boosting, AdaBoost and random forests	2000–N/A	3,111
Jones	Peat and Jones (2012)	N/A	Australia	The study adds to current debates by investigating the performance of N.N.s in the context of forecast combination. Furthermore, to test the performance of the N.N. model with the most widely used discrete choice model in the bankruptcy literature, logistic regression	Period 1: 2000–2002, Period 2: 2003+	558 max/period (different samples for different periods)
Jones	Jones (2017)	3.91	USA	To outline a conspicuous trend in the literature by applying the gradient boosting model	1987–2013	1,115
Jones	Cheng, Jones, and Moser (2018)	0.27	USA	To examine the trading behaviour of U.S. corporate insiders and certain groups of institutional investors (short-term, transient, top-performing, and those with fiduciary responsibility) in the eight quarters leading up to a U.S. firm bankruptcy filing	1992–2012	610
Jones	Jones and Wang (2019)	2.16	Whole world	The study utilizes an advanced machine learning method known as TreeNet(R) (Salford Systems, 2017) to predict various private company failure states, ranging from binary settings (i.e. failed vs non-failed) to more complex multi-class settings with up to five states of failure	2009–2013	4,922,271
Jones	Alam, Gao, and Jones (2021)	0.97	North America	To propose a deep learning model of firm failure prediction and compare it to the traditional prediction model	2001–2018	641,667
Li, Sun	Sun, Li, Huang, and He (2014)	5.97	China	To compile a complete summary, analysis and evaluation of the current literature on financial distress prediction (FDP)	N/A	No real data
Li, Sun	Li and Sun (2009)	2.06	China	To construct of hybrid case-based reasoning model and to test the performance	N/A	153
Li, Sun	Li and Sun (2011a)	N/A	China	To explain the data mining technique of two-step clustering; to introduce a new mining method	N/A	266
Li, Sun	Li and Sun (2011b)	1.30	China	To explain the necessity to base such case-based reasoning ensemble (CBRE) prediction technique on random similarity functions (RSF)	N/A	313
Li, Sun	Li and Sun (2011c)	1.52	China	To construct a principal-component case-based reasoning ensemble (PC-CBR-E) model	N/A	270
Li, Sun	Li and Sun (2011d)	0.36	China	To compare different models using SVM techniques	N/A	153
Li, Sun	Li, Lee, Zhou, and Sun (2011)	1.17	China	To construct a new model based on random subspace binary logistic regression analysis	N/A	270
Li, Sun	Li and Sun (2012)	1.01	China	To compare the CBR ensemble with MDA, logistic regression, and classical CBR algorithm	N/A	153
Li, Sun	Li, Hong, He, Xu, and Sun (2013)	0.40	China	To construct a small sample-oriented case-based kernel predictive method (SSOCBKPM)	N/A	200
Li, Sun	Li, Li, Wu, and Sun (2014)	2.42	China	To verify statistically a performance of statistic-based wrapper based on SVM methods	N/A	668
Li, Sun	Sun et al. (2014)	0.97	China	To explore the “imbalanced FDP based on SVM.”	Sample 1:2010–2012Sample 2:2012–2013	427
Li, Sun	Sun et al. (2016)	0.79	China, world	To propose an approach for dynamic evaluation and prediction of financial distress based on the entropy-based weighting (EBW), the support vector machine (SVM) and an enterprise’s vertical sliding time window (VSTW)	2006–2010	5
Li, Sun	Sun et al. (2019)	0.56	China	To replicate the Campbell, Hilscher, and Szilagyi (2008) bankruptcy prediction model and add additional terms for the absolute value of changes in the percentage ownership by corporate insiders over the previous six months or changes in ownership by specific groups of institutional investors	2000–2015	486
Li	Li, Hong, Zhou, and Yu (2015)	0.12	China	To compare pure SVM, hybrid SVM, SVM ensemble, and hybrid SVM ensemble	N/A	551
Li	Li, Xu, and Yu (2017)	0.57	China	To provide a “feasible approach to handle possible mixed information caused by oversampling; mixed sample modelling (MSM).”	N/A	No real data
du Jardin	du Jardin (2016)	3.51	France	To present a new model of bankruptcy prediction based on ensembles of models	2002–2012 Learning samples 2002–2011 (one set per year), testing samples 2003–2012 (one set per year)	337,400
du Jardin	du Jardin and Séverin (2012)	0.82	France	To introduce a new way of using a Kohonen map as a prediction model	Period 1: 1998–2000, period 2: 2000–2002, period 3: 2002–2004	11,540
du Jardin	du Jardin (2018)	1.62	France	To propose a new bankruptcy prediction model that relies on estimating failure patterns that are quantified with ensembles of Kohonen maps	2007–2014	6,120
du Jardin	du Jardin et al. (2019)	1.35	France	To present a new measure that helps improve bankrupt models’ accuracy by using a method to embody earnings management	Period 1: 2006–2007, Period 2: 2009–2010, Period 3: 2011–2012	14,220 max (different samples for different periods)
du Jardin	du Jardin (2021)	1.25	France	To present a new method of bankruptcy prediction based on modelling firms’ history with a self-organizing map. To propose an approach that relies on a particular modelling of firm history using self-organizing neural networks and segmentation of the data space, which makes it possible to typify subsets of firms that share a common evolution of their financial situation over time	Period 1: 2000–2003, Period 2: 2004–2007, Period 3: 2007–2011, Period 4: 2011–2015	293,840
du Jardin	du Jardin (2021)	2.82	France	To present a new firm-failure forecasting method using an ensemble of self-organizing neural networks	2008–2015	470,330
Korol	Korol (2013)	2.61	Poland, Latin America (Mexico, Argentina, Brazil, Chile, Peru)	To compare “the effectiveness of twelve different early warning models.”	Poland 2000–2007, Latin America 1996–2009	245
Korol	Korol and Kolodi (2011)	1.15	Poland	To present a fuzzy logic-based system	1999–2005	132
Korol	Korol (2018)	0.16	7 EU countries, ten non-EU countries	To evaluate the effectiveness of the 13 fuzzy logic models	Sample 1: 1999–2007, Sample 2: 2000–2009, Sample 3: 1999–2009	166
Korol	Korol (2019)	N/A	EU	To develop and evaluate dynamic bankruptcy prediction models for European enterprises	2004–2017, the period ten years before bankruptcy	600
Karas, Režňáková	Karas and Režňáková (2017)	1.84	Czech Republic	To verify whether bankruptcy predictors are specific in terms of industry or time	2004–2013 (the concerned companies went bankrupt 2008–2013)	34,533
Karas, Režňáková	Režňáková and Karas (2015)	0.97	V4 (Czech Rep., Hungary, Poland, Slovakia)	To test the predictive capability of the original version of the Altman model in an environment different from the environment of its origin and to explore its transferability to a different economic environment	2007–2012	5,977
Karas, Režňáková	Karas et al. (2017)	0.50	Czech Republic	To analyse the current accuracy of four traditional bankruptcy prediction models (the revised Z-score model, Altman-Sabato’s model in both versions – with unlogged and logged predictors, and IN 05 model) in agriculture	2011–2014	475
Karas, Režňáková	Karas and Režňáková (2017)	1.16	Czech Republic	To create a bankruptcy prediction model based on the data from construction companies in the Czech Republic	2011–2014	654
Karas, Režňáková	Karas and Režňáková (2018)	0.53	Czech Republic	To analyze the usefulness of information about the past development of a company’s financial situation in predicting bankruptcy	2011–2014	1,355
Karas	Karas and Srbová (2019)	0.52	Czech Republic	To test the current accuracies of five selected bankruptcy models in predicting the bankruptcy of construction companies and to create a new model designed specifically for this branch	2006–2015 (the concerned companies went bankrupt 2011–2015)	4,420
Karas, Režňáková	Karas and Režňáková (2020)	0.41	Czech Republic	To introduce a new hybrid model incorporating solely cashflow-based indicators (three model versions were derived)	2013–2018	4,350
Karas, Režňáková	Karas and Režňáková (2021)	1.25	EU-28	Construct a default prediction model incorporating factors considered internal or external manifestations of the financial constraint situation. For example, authors use the Cox semiparametric model, leaving the baseline hazard rate unspecified and employing macroeconomic variables as explanatory variables	2014–2019	213,731

Note(s): By core authors within the period 2010–2022; sorted in descending order by the FWCI indicator

Source(s): Appendix created by authors

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